Efficient Prediction of Heart Disease Using Machine Learning Algorithms ‎With Winsorized ‎ and Logarithmic Transformation Methods‎ for Handling ‎Outliers Data

Heart disease is a prevalent and life-threatening condition that poses significant challenges to ‎healthcare ‎systems worldwide. Accurate and timely diagnosis of heart disease is crucial for effective ‎treatment and ‎patient management. In recent years, machine learning algorithms have emerged as ‎powerful tools for ‎predicting and identifying individuals at risk of heart disease. This article ‎highlights the importance of ‎heart disease diagnosis and explores the potential of machine learning ‎algorithms in enhancing ‎the diagnosis of heart disease accuracy. This article presents a study to ‎develop a model for predicting heart ‎disease in the Cleveland patient dataset. The innovation of this ‎research involved identifying ‎and handling outliers data using Winsorized and Logarithmic ‎transformation methods. We also used ‎Wrapper and Embedded methods to determine the most ‎critical features for diagnosing heart disease. ‎In addition to the usual features, Exercise-induced ‎angina and No. of major vessels were found to be ‎important. We then compared the performance of ‎four machine learning algorithms, including KNN, ‎Naïve Bayes Classifier, Decision Tree, and ‎Support Vector Classifier to determine the best algorithm ‎for predicting heart disease. The findings ‎showed that the Decision Tree algorithm had the best ‎performance with an accuracy of 97.95%. ‎Overall, this study provides insights into developing an ‎accurate model for predicting heart disease, ‎which could help improve the diagnosis and treatment of ‎this condition.‎